• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.6
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• pp.722-728
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• 2003

In this paper, we propose a fuzzy traffic controller of Sugeno`s fuzzy model so as to model the nonlinear characteristics of controlling the traffic light. It use a degree of the traffic congestion of the preceding roads as an input so that it can cope with traffic congestion appropriately, which causes the loss of fuel and our discomfort. First, in order to construct fuzzy traffic controller of Sugeno`s fuzzy model, we model the control process of the traffic light by using Mamdani`s fuzzy model, which has the uniform membership functions of the same size and shape. Second, we make Mamdani`s fuzzy model with the non-uniform membership functions so that it can exactly reflect the knowledge of experts and operators. Last, we construct the fuzzy traffic controller of Sugeno`s fuzzy model by learning from the input/output data, which is retrieved from Mamdani`s fuzzy model with the non-uniform membership functions. We compared and analyzed the fixed traffic light controller, the fuzzy traffic controller of Mamdani`s fuzzy model and the fuzzy traffic controller of Sugeno`s fuzzy model by using the delay time and the proportion of the entered vehicles to the occurred vehicles. As a result of comparison, the fuzzy traffic controller of Sugeno`s fuzzy model showed the best performance.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.1 no.1
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• pp.1-5
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• 2001

In this paper, we propose a fuzzy traffic controller that is able to cope with traffic congestion appropriately. In order to consider such situation as loss of green time caused by spillback of upper crossroad, it imports a degree of traffic congestion of upper roads which vehicles on a crossroad are to proceed to. We constructed the equal-partitioned fuzzy traffic controller that uses the membership functions of the same size and shape, and modified the size and shape, and modified the size and shape of its membership functions by the membership function modification algorithm. In experiment, we compared and analyzed the fixed signal controller, the fuzzy traffic controller with the membership of the same size and shape, and the modified fuzzy traffic controller by using the delay time, the proportion of entered vehicles to occurred vehicles and the proportion of passed vehicles to entered vehicles. As a result of experiment, the modified fuzzy controller showed more enhanced performance than others.

• Journal of Korean Society of Transportation
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• v.14 no.2
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• pp.89-118
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• 1996

The development and implementation of a real-time, traffic adaptive control scheme based on fuzzy logic through Video Image Detector systems (VIDs) is presented. Through VIDs based image processing, fuzzy logic can be used for a real-time traffic adaptive signal control scheme. Fuzzy control logic allows linguistic and inexact traffic data to be manipulated as a useful tool in designing signal timing plans. The fuzzy logic has the ability to comprehend linguistic instructions and to generate control strategy based on a priori verbal communication. The implementation of fuzzy logic controller for a traffic network is introduced. Comparisons are made between implementations of the fuzzy logic controller and the actuated controller in an isolated intersection. The results obtained from the application of the fuzzy logic controller are also compared with those corresponding to a pretimed controller for the coordinated intersections. Simulation results from the comparisons indicate the performance of the system is between under the fuzzy logic controller. Integration of the aforementioned schemes into and ATMS framework will lead to real-time adjustment of the traffic control signals, resulting in significant reduction in traffic congestion.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.664-667
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• 2003

We propose a frizzy traffic controller of Sugeno's fuzzy model so as to model the nonlinear characteristics of controlling the traffic light. It uses a degree of the traffic congestion of the preceding roads as an input so that it can cope with traffic congestion appropriately, which causes the loss of fuel and our discomfort. In order to construct fuzzy traffic controller of Sugeno's fuzzy model we first model the control process of the traffic light by using Mamdani's fuzzy model, which has the uniform membership functions of the same size and shape. Next we make Mamdani's fuzzy model with the non-uniform membership functions so that it can exactly reflect the knowledge of experts and operators. Lastly, we construct the fuzzy traffic controller of Sugeno's fuzzy model by learning from the input/output data, which is retrieved from Mamdani's fuzzy model with the non-uniform membership functions. We compared and analyzed the service level of the traffic light controllers by using the delay time. As a result of comparison, the fuzzy traffic controller of Sugeno's fuzzy model shows the best service level of them.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.735-738
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• 1997

The traffic congestion caused by the exploding increase of vehicles became one of the severest social problems. Among the various approaches to solve this problem, controlling the length of traffic signals appropriately according to the individual traffic situation would be the most plausible and cost-effective method. To design a traffic signal controller which has such a property as adaptive decision-making process, we adopt fuzzy logic control method(fuzzy traffic signal controller), Moreover, using genetic algorithms we obtain an optimized fuzzy traffic signal controller (GA-fuzzy traffic signal controller). To evaluate and validate the proposed fuzzy and GA-fuzzy traffic signal controller, simulation results are presented.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.6
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• pp.100-108
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• 1993

In this paper, noticing the point of human's ability which appropriately cope with vague conditions, we design fuzzy traffic signal light controller similar to human's distinction ability and decide the optimum cycle most suited to any traffic junction using fuzzy identification algorithm. In this study, for the control output decision process we design fuzzy controller better than electronic vehicle actuated controller in performance. We propose the cycle decision method which is not limited by the variance of traffic junction vehicle number through overcoming the limit of Webster's method which is adopted by the fixed cycle controller. Simulated experimental results show that fuzzy controller and fuzzy identification algorithm are better than the existing electronic vehicle actuated controller and fixed cycle controller in delay time per vehicle.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.6
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• pp.1592-1598
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• 2008

Fixed cycle traffic signal controller is pattern type controller. This method is that one concluding traffic proposal affect fixed cycle controller to deciding cycle time during 24 hours. Traffic intersection jam is caused traffic controller supervisor input wrong traffic proposal. The available to traffic controller is traffic proposal is not selected by an outward look but propriety traffic proposal is compared with each other. This paper presents that property traffic proposal adopted to fixed cycle controller by fuzzy analytic hierachy process and new proposal adopted fixed cycle controller is compared with others.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.907-910
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• 1993

This paper describes the use of fuzzy control and decision making to simulate the control of traffic flow at an intersection. To show the value of fuzzy logic as an alternative method for control of traffic environments. A traffic environment includes the lanes to and from an intersection, the intersection, vehicle traffic, and signal lights in the intersection. To test the fuzzy logic controller, a computer simulation was constructed to model a traffic environment. A typical cross intersection was chosen for the traffic environment, and the performance of the fuzzy logic controller was compared with the performance of two different types of conventional control. In the hardware verifications, fuzzy logic was used to control acceleration of a model train on a circular path. For the software experiment, the fuzzy logic controller proved better than conventional control methods, especially in the case of highly uneven traffic flow between different directions. On the hardware si e of the research, the fuzzy acceleration control system showed a marked improvement in smoothness of ride over crisp control.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.364-364
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• 2000

Timed Petri Net(TPN) is one of methods to model and to analyze Discrete Event Dynamic Systems(DEDSs) with real time values. It has two time values, earliest firing time ($\alpha$$_{i}$) and latest firing time ($\beta$$_{I}$) for the each transition. A transition of TPN is fired at arbitrary time of time interval ($\alpha$$_{I}$, $\beta$$_{i}$). Uncertainty of firing time gives difficulty to analyze and estimate a modeled system. In this paper, we proposed the Fuzzy Transition Timed Petri Net(FTTPN) with fuzzy theory to determine the optimal transition time (${\gamma}$$_{i}$). The transition firing time (${\gamma}$$_{i}$) of FTTPN is determined from fuzzy controller which is modeled with information of state transition. Each of the traffic signal controllers are modeled using the proposed method and timed petri net. And its Performance is evaluated by simulation of traffic signal controller. controller.

• Journal of Korean Society of Transportation
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• v.14 no.1
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• pp.117-133
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• 1996

In the paper a superior performance algorithm compared to the existing vehicle actuated controller and time fixed controller and the additional controller is described through realization of fuzzy traffic phase controller. Fuzzy theory is encouraging since the application is similar to human's decision ability that is approately coped with uncertain conditions. The paper presents that selection of the phase adequated the variable traffic conditions through the fuzzy theory algorithm and decision of optimal cycle time approated the uncertain traffic volume are predominant in traffic jam solution compared to the existing Webster's cycle time decision method and the sequential traffic phase design method and dual-ring phase operation system.